Low-level Model Research Articles

Bi-level Optimal Operation Model of Mobile Energy Storage System in Coupled Transportation-power Networks

The operation characteristics of energy storage can help the distribution network absorb more renewable energy while improving the safety and economy of the power system. Mobile energy storage systems (MESSs) have a broad application market compared with stationary energy storage systems and electric vehicles due to their flexible mobility and good dispatch ability. However, when urban traffic flows rise, the congested traffic environment will prolong the transit time of MESS, which will ultimately affect the operation state of the power networks and the economic benefits of MESS. This paper proposes a bi-level optimization model for the economic operation of MESS in coupled transportation-power networks, considering road congestion and the operation constraints of the power networks. The upper-level model depicts the daily operation scheme of MESS devised by the distribution network operator (DNO) in order to maximize the total revenue of the system. With fuzzy time windows and fuzzy road congestion indexes, the lower-level model optimizes the route for the transit problem of MESS. Therefore, road congestion that affects the transit time of MESS can be fully incorporated in the optimal operation scheme. Both the IEEE 33-bus distribution network and the 29-node transportation network are used to verify and examine the effectiveness of the proposed model. The simulation results demonstrate that the operation scheme of MESS will avoid the congestion period when considering road congestion. Besides, the transit energy consumption and the impact of the traffic environment on the economic benefits of MESS can be reduced.

Bi-Level Energy Management Model of Grid-Connected Microgrid Community

As the proportion of renewable energy power generation continues to increase, the number of grid-connected microgrids is gradually increasing, and geographically adjacent microgrids can be interconnected to form a Micro-Grid Community (MGC). In order to reduce the operation and maintenance costs of a single micro grid and reduce the adverse effects caused by unnecessary energy interaction between the micro grid and the main grid while improving the overall economic benefits of the micro grid community, this paper proposes a bi-level energy management model with the optimization goal of maximizing the social welfare of the micro grid community and minimizing the total electricity cost of a single micro grid. The lower-level model optimizes the output of each equipment unit in the system and the exchange power between the system and the external grid with the goal of minimizing the operating cost of each microgrid. The upper-level model optimizes the goal of maximizing the social welfare of the microgrid. Taking a microgrid community with four microgrids as an example, the simulation analysis shows that the proposed optimization model is beneficial to reduce the operating cost of a single microgrid, improve the overall revenue of the microgrid community, and reduce the power interaction pressure on the main grid.

Improving the Resilience of Port–Hinterland Container Logistics Transportation Systems: A Bi-Level Programming Approach

Port–hinterland container logistics transportation systems (PHCLTSs) are significant to economic and social development. However, various kinds of unconventional emergency events (UEEs), such as natural or human-caused disasters, threaten PHCLTSs. This study aims to measure and improve the resilience of PHCLTSs. Bi-level programming models with two different lower level models are established to help PHCLTSs recover their capacity efficiently in the face of UEEs. In the upper level model, the government makes immediate recovery decisions about a damaged PHCLTS with the goal of improving the resilience of the PHCLTS. In the lower level models, truck carriers make decisions about transportation routes and freight volume in the recovered PHCLTS. They cooperate fully to pursue the maximization of total profit and are coordinated by a central authority, or they make their own decisions to pursue maximization of their own profit noncooperatively. An algorithm combining particle swarm optimization (PSO) and traditional optimization algorithms is proposed to solve the bi-level programming models. The numerical experimental results show the validity of the proposed models.

Optimal participation of ADN in energy and reserve markets considering TSO-DSO interface and DERs uncertainties

The flexibility of distribution networks continues to thrive due to the increasing installment of distributed energy resources (DERs). In addition to meeting the load demand in active distribution networks (ADNs), DERs can also provide energy and reserve for the upper-layer grid (i.e., sub-transmission network) at their connection node by participating in the energy and reserve markets. This paper proposes a novel optimal participation model of ADN in energy and reserve markets that takes into account the uncertainties of DERs and the interface of transmission system operator (TSO) and distribution system operator (DSO). The problem of DSO’s strategic behavior is formulated as a stochastic bi-level optimization model. The upper-level model indicates the market clearing of ADNs managed by the DSO and the lower-level model represents the energy and reserve market clearing of the upper-layer grid managed by the TSO. The nonlinear bi-level model is converted into a mathematical program with equilibrium constraint (MPEC) model, and then the mixed-integer second order cone programming (MISOCP) model based on Karush-Kuhn-Tucker conditions and strong duality theory. The effectiveness of the proposed model on improving the economy of ADN and the utilization rate of DERs is validated by numerical studies.

Semantic enrichment approach for low-level CAD models managed in PLM context: Literature review and research prospect

The proposed approach in the paper is dedicated to enrichment of CAD models storage in PLM (Product Lifecycle Management) systems or archive databases. The paper considers low level CAD models (i.e. frozen geometry and without CAD model tree) based on standards such as STL, IGES and STEP AP203 first edition where the CAD model tree is absent. The paper also highlights the differences in semantic richness between these standards and their degrees of industrial implementation. STEP AP242 standard is considered as a high level representation for CAD files. The paper aims to review the literature that addresses the methods for semantic enrichment of CAD models by using ontologies. The future challenges and one possible research direction are then discussed. A first application called VAQUERO for CAD enrichment using an ontology based on STEP AP242 standard is proposed.

Generation Expansion Planning Considering Competition and Market Power

One of the main goals of establishing electricity markets is to increase efficiency, and another is to lower the prices of electricity by ensuring competition. Protecting and improving the competitive environment of the market help in achieving these goals. If any of the generation companies in the market can exercise market power, the competition will then decrease and may even disappear. This study offers an optimization model minimizing the market power of all companies. The model can be utilized from the beginning of the liberalization of the electricity sector or during the transformation process from the monopoly to the competitive markets. It is a mixed-integer linear programming model where the bi-level structure of the problem is transformed so that the lower-level and the upper-level are combined in a single level. The lower-level model minimizes investment and production costs of companies. The upper-level maximizes the competition between all companies by minimizing market power. A numerical example is presented and discussed to test the effectiveness of the proposed model and to evaluate the results. Two scenarios are examined in which companies do and do not have budget restrictions. The results show that the model works successfully in both scenarios, and the market power of all companies is decreased so that none can distort competition. However, the second scenario, in which the state-owned company has a budget limit, is more successful in terms of satisfying not only the state and private companies but also consumers. The companies that were small at the beginning have higher shares in the second scenario and the shares of bigger companies are decreased significantly. Also, the second scenario was able to meet the investment needs from a lower level, and investments were shifted to private firms, thus saving investment expenditures of both government and private firms. Furthermore, in both scenarios, prices tend to decrease toward the target year, but the second scenario can keep prices much lower.

Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach

In the context of transition towards cleaner and sustainable energy production, microgrids have become an effective way for tackling environmental pollution and energy crisis issues. With the increasing penetration of renewables, how to coordinate demand response and renewable generations is a critical and challenging issue in the field of microgrid scheduling. To this end, a bi-level scheduling model is put forward for isolated microgrids with consideration of multi-stakeholders in this paper, where the lower- and upper-level models respectively aim to the minimization of user cost and microgrid operational cost under real-time electricity pricing environments. In order to solve this model, this research combines Jaya algorithm and interior point method (IPM) to develop a hybrid analysis-heuristic solution method called Jaya-IPM, where the lower- and upper-levels are respectively addressed by the IPM and the Jaya, and the scheduling scheme is obtained via iterations between the two levels. After that, the real-time prices updated by the upper-level model and the electricity plans determined by the lower-level model will be alternately iterated between the upper- and lower-levels through the real-time pricing mechanism to obtain an optimal scheduling plan. The test results show that the proposed method can coordinate the uncertainty of renewable generations with demand response strategies, thereby achieving a balance between the interests of microgrid and users; and that by leveraging demand response, the flexibility of the load side can be fully exploited to achieve peak load shaving while maintaining the balance of supply and demand. In addition, the Jaya-IPM algorithm is proven to be superior to the traditional hybrid intelligent algorithm (HIA) and the CPLEX solver in terms of optimization results and calculation efficiency. Compared with the HIA and CPLEX, the proposed method improves the MG net revenue by 10.9% and 11.9%, and reduces the user cost by 6.1% and 7.7%; our approach decreases the calculation time by about 90% and 60%.

Active Safety Planning Method for Transportation Network Design

Traffic accidents are frequent although various countermeasures are introduced. Traffic safety cannot be fundamentally improved if it is not considered in the transportation network design stage. Although it is well known that traffic safety is one of the most important concerns of the public, traffic safety is not adequately accommodated in transportation planning. This paper considers traffic safety as a major criterion in designing a transportation network. It is a kind of proactive measure rather than reactive measure. A bilevel programming model system is proposed where the upper level is the urban planners’ decision to minimize the estimated total number of traffic accidents, and the lower level is the travelers’ response behaviors to achieve transportation system equilibrium. A genetic algorithm (GA) with elite strategy is proposed to solve the bilevel model. The method of successive averages (MSA) is embedded for the lower level model, which is a feedback procedure between destination choice and traffic assignment. To demonstrate the effectiveness of the proposed method and algorithm, an experimental study is carried out. The results show that these methods can be a valuable tool to design a safer transportation network although efficiency, in terms of system total travel time, is slightly sacrificed.

Sensor Deployment Strategy and Traffic Demand Estimation with Multisource Data

Since traffic origin-destination (OD) demand is a fundamental input parameter of urban road network planning and traffic management, multisource data are adopted to study methods of integrated sensor deployment and traffic demand estimation. A sensor deployment model is built to determine the optimal quantity and locations of sensors based on the principle of maximum link and route flow coverage information. Minimum variance weighted average technology is used to fuse the observed multisource data from the deployed sensors. Then, the bilevel maximum likelihood traffic demand estimation model is presented, where the upper-level model uses the method of maximum likelihood to estimate the traffic demand, and the lower-level model adopts the stochastic user equilibrium (SUE) to derive the route choice proportion. The sequential identification of sensors and iterative algorithms are designed to solve the sensor deployment and maximum likelihood traffic demand estimation models, respectively. Numerical examples demonstrate that the proposed sensor deployment model can be used to determine the optimal scheme of refitting sensors. The values estimated by the multisource data fusion-based traffic demand estimation model are close to the real traffic demands, and the iterative algorithm can achieve an accuracy of 10−3 in 20 s. This research has significantly promoted the effects of applying multisource data to traffic demand estimation problems.

Optimal sizing and operations of shared energy storage systems in distribution networks: A bi-level programming approach

Rather than using individually distributed energy storage frameworks, shared energy storage is being exploited because of its low cost and high efficiency. However, proper sizing and operations approaches are still required to take advantage of shared energy storage in distribution networks. This paper proposes a bi-level model to optimize the size and operations of shared energy storage in hybrid renewable-resource power generation systems. The upper-level model maximizes the benefits of sharing energy storage for the involved stakeholders (transmission and distribution system operators, shared energy storage operators and the various power plant owners) and the lower-level model minimizes the hybrid system operating costs. The benefits of this system were found to be: (1) reductions in wind and solar power curtailment and coal-fired generation costs; (2) peak shaving; (3) frequency regulation; and (4) a deferral of upgrades to power transmission and distribution facilities. To fully realize the long-term planning and short-term operational interactions of shared energy storage, a bi-level nested genetic algorithm was designed to solve the proposed model. By continuously updating the solutions, a set of optimal solutions were generated. To validate the approach, numerical tests were conducted, with the results showing that by properly sizing and operating the shared energy storage in distribution networks, the wind curtailment rate was reduced by about 10.2%, the solar curtailment rate was reduced by 14.2%, and the stakeholder benefits were around 154 million dollars. Further discussion is given on the benefits of shared energy storage investments.

Optimal Deployment of Cordon Sanitaire with Available Testing Capacity

During the COVID-19 pandemic, authorities in many places have implemented various countermeasures, including setting up a cordon sanitaire to restrict population movement. This paper proposes a bi-level programming model to deploy a limited number of parallel checkpoints at each entry link around the cordon sanitaire to achieve a minimum total waiting time for all travelers. At the lower level, it is a transportation network equilibrium with queuing for a fixed travel demand and given road network. The feedback process between trip distribution and trip assignment results in the predicted waiting time and traffic flow for each entry link. For the lower-level model, the method of successive averages is used to achieve a network equilibrium with queuing for any given allocation decision from the upper level, and the reduced gradient algorithm is used for traffic assignment with queuing. At the upper level, it is a queuing network optimization model. The objective is the minimization of the system’s total waiting time, which can be derived from the predicted traffic flow and queuing delay time at each entry link from the lower-level model. Since it is a nonlinear integer programming problem that is hard to solve, a genetic algorithm with elite strategy is designed. An experimental study using the Nguyen-Dupuis road network shows that the proposed methods effectively find a good heuristic optimal solution. Together with the findings from two additional sensitivity tests, the proposed methods are beneficial for policymakers to determine the optimal deployment of cordon sanitaire given limited resources.

Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation Using Iterative Learning.

A hybrid exoskeleton comprising a powered exoskeleton and functional electrical stimulation (FES) is a promising technology for restoration of standing and walking functions after a neurological injury. Its shared control remains challenging due to the need to optimally distribute joint torques among FES and the powered exoskeleton while compensating for the FES-induced muscle fatigue and ensuring performance despite highly nonlinear and uncertain skeletal muscle behavior. This study develops a bi-level hierarchical control design for shared control of a powered exoskeleton and FES to overcome these challenges. A higher-level neural network–based iterative learning controller (NNILC) is derived to generate torques needed to drive the hybrid system. Then, a low-level model predictive control (MPC)-based allocation strategy optimally distributes the torque contributions between FES and the exoskeleton’s knee motors based on the muscle fatigue and recovery characteristics of a participant’s quadriceps muscles. A Lyapunov-like stability analysis proves global asymptotic tracking of state-dependent desired joint trajectories. The experimental results on four non-disabled participants validate the effectiveness of the proposed NNILC-MPC framework. The root mean square error (RMSE) of the knee joint and the hip joint was reduced by 71.96 and 74.57%, respectively, in the fourth iteration compared to the RMSE in the 1st sit-to-stand iteration.

From Hierarchical Control to Flexible Interactive Electricity Services: A Path to Decarbonization

We propose to conceptualise electric energy systems as complex dynamical systems using physically intuitive multilayered energy modelling as the basis for systematic diverse technology integration, and control in on-line operations. It is shown that such modelling exhibits unique structure which comes from the conservation of instantaneous power (P) and of instantaneous reactive power ( _Q), (interaction variables (intVar)) at the interfaces of subsystems. The intVars are used as a means to model and control the interactive zoomed-out inter-modular (inter-area, inter-component) system dynamics. Control co-design can then be pursued using these models so that the primary control shapes intVars of its own module by using its own lowlevel detailed technology-specific model and intVar info exchange with the neighbours. As a result, we describe how the proposed approach can be used to support orderly evolution from today’s hierarchical control to a platform enabling flexible interactive protocols for electricity services. The potential for practical use of the proposed concepts is far-reaching and transparent. All that needs to be conceived is that intVar characterising any intelligent Balancing Authority (iBA) is a generalisation of today’s Area Control Error (ACE) characterising net energy balance of a Balancing Authority (BA). An iBA can be any subsystem with its own sub-objectives, such as distributed energy resources (DERs) comprising customers and grid forming microgrids; distribution systems; transmission systems; Independent System Operators (ISOs); and, ultimately, electric energy markets within large interconnection. Several industry problems are described as particular sub-problems of general interactive electricity services. These formulations help one compare models and assumptions used as part of current solutions, and propose enhanced solutions. Most generally, feasibility and stability conditions can be introduced for ensuring feasible power flow solutions, regulated frequency and voltage and orderly power exchange across the iBAs.

A hierarchical analysis of ecosystem classification with implementing in two continental ecoregions

BackgroundThe ecosystem classification of land (ECL) has been studied for a couple of decades, from the beginning of the perfect organism system “top-down” approach to a reversed “bottom-up” approach by defining a micro-ecological unit. After comparing two cases of the ecosystem classification framework implemented in the different continental ecoregions, the processes were carefully examined and justified.ResultsTheoretically, Bailey’s upper levels of ECL (Description of the ecoregions of the United States, 2nd ed. Rev and expanded (1st ed. 1980). Misc. Publ. No. 1391 (Rev). Washington DC USDA Forest Service; 1995) were applied to the United States and world continents. For the first time, a complete ECL study was accomplished in Western Utah of the United States, with eight upper levels of ECOMAP (National hierarchical framework of ecological units. U.S. Department of Agriculture, Forest Service, Washington, DC. https://www.researchgate.net/publication/237419014_National_hierarchical_framework_of_ecological_units; 1993) plus additional ecological site and vegetation stand. China’s Eco-geographic classification was most likely fitted into Bailey’s Ecosystem Classification upper-level regime. With a binary decision tree analysis, it had been validated that the Domains have an empty entity for 500 Plateau Domain between the US and China ecoregion framework. Implementing lower levels of ECL to Qinghai Province of China, based on the biogeoclimatic condition, vegetation distribution, landform, and plant species feature, it had classified the Section HIIC1 into two Subsections (labeled as i, ii), and delineated iia of QiLian Mountain East Alpine Shrub and Alpine Tundra Ecozone into iia-1 and iia-2 Subzones. Coordinately, an Ecological Site was completed at the bottom level.Conclusions(1) It was more experimental processing by implementing a full ECL in the Western Utah of the United States based on the ECOMAP (1993). (2) The empty entity, named as Plateau Domain 500, should be added into the top-level Bailey’s ecoregion framework. Coordinately, it includes the Divisions of HI and HII and the Provinces of humid, sub-humid, semiarid, and arid for China's Eco-Geographic region. (3) Implementing a full ECL in a different continent and integrating the lower level's models was the process that could handle the execution management, interpreting the relationship of ecosystem, dataset conversion, and error correction.

Routing Optimization for Road Administration Vehicles with Consideration of Overloaded Truck Detour Behavior on Rural Highways

Abstract In response to the increasingly frequent detour behavior of overloaded trucks attempting to avoid overload inspection and punishment, this article develops a bilevel programming model to optimize the routes of road administration vehicles by simulating the capture and anticapture interaction between administration vehicles and overloaded trucks. The upper-level model determines the optimal patrol routes of administration vehicles to maximize the number of overloaded trucks that can be captured. The lower-level model deduces the detour routes of overloaded trucks based on their circumvention behavior relative to administration vehicles. To solve the bilevel programming model, this article proposes a heuristic algorithm combining the ant colony algorithm and the labeling algorithm. To validate the proposed model and algorithm, this article uses actual rural highway data for Guiyang, China. The result proves the feasibility and superiority of the proposed programming model and algorithm. Compared to traditional overload management techniques, considering the detour behavior of overloaded trucks in optimizing the routing of road administration vehicles improves the effectiveness of overload management by up to 1.65 times.

IGDT-Based Medium-Term Optimal Cascade Hydropower Operation in Multimarket with Hydrologic and Economic Uncertainties

Hydrologic uncertainty and economic uncertainty both affect market-oriented hydropower operation. For medium-term cascade hydropower operation, the uncertainties of daily reservoir inflow and market clearing power price can pose risks to operation, especially when power stations participate in multiple power markets. It is difficult to obtain the fluctuation range of uncertain variables for different expected power sale profits, and to plan the operation range of cascade hydropower stations. For a cascade hydropower system dispatching power to two markets, this work developed a risk assessment method for medium-term optimal cascade hydropower operation based on information gap decision-making theory (IGDT). We established a bilevel stochastic IGDT model for the main hydrologic and economic uncertainties in hydropower market participation. The lower-level model solves the minimum robustness profit or maximum opportunity profit from power sale, and the upper-level model solves the maximum robustness fluctuation range or minimum opportunity fluctuation range of the uncertain variables for different expected profits. Then we converted the bilevel model to two equivalent single-level models, and provided solutions. The proposed method does not require the probability distribution of uncertain variables, which is often unavailable; moreover, it reduces computational complexity while ensuring accuracy of results. The proposed method can provide decision makers with reasonable power dispatch options. This work used a cascade hydropower system in Southwest China as a case study. The proposed method provided acceptable fluctuation range of uncertain reservoir inflow and power price under different expected profits, and provided the operation range to guarantee the expected profit. Decision makers with different risk preferences can evaluate various strategies to optimize medium-term cascade hydropower operation to ensure the expected profit.

An economic analysis of integrating bike sharing service with metro systems

• Examine cases where the bike sharing and metro operators work either independently or jointly. • Examine user travel choices against the bike and metro operation decisions given total demand. • Bike sharing helps reduce total social cost even if the operator maximizes its profit. • A more attractive metro service can help bike sharing to be more profitable. This paper develops an analytical bi-level model to examine the strategic interaction and potential integration between bike sharing operation and metro system operation. In the bi-level problem with a given total travel demand, the lower-level model quantifies users’ equilibrium travel choices and the upper-level model optimizes the operation decisions of the bike sharing operator and metro system operator. We examine different operation regimes, where the bike sharing operator and metro operator may work either independently or jointly. We also conduct numerical analysis in the context of Sydney. The analytical and numerical results show that bike sharing can help reduce total social cost even if the operator maximizes its profit. A more attractive metro service may also help the bike sharing operator to increase its profit. Coordinated operation of bike sharing and metro system can help further reduce total social cost while the bike sharing operator can improve its profit.

Morning Peak-Period Pricing Surcharge of Elderly Passengers Taking Express Buses

This study deals with the elderly fare pricing issue for taking express buses in the morning peak period. As many elderly passengers are not commuters, fare discount policy may not be an opportune option when buses get overcrowded. Imposing surcharge on the elderly becomes a potentially beneficial measure that encourages an appropriate number of elderly passengers to circumvent the most crowded buses. The elderly pricing surcharge problem is formulated as a bilevel model, in which the upper-level model is to make the pricing surcharge decision, and the lower-level model is the equilibrium passenger assignment that represents passengers’ bus choice behavior. It is classified into the special case and the generic case depending on the number of buses that impose surcharge. Several useful properties of two cases are analyzed, and a trial-and-error solution method is later developed to solve these two cases. Numerical experiments show that the elderly pricing surcharge scheme is not always applicable to all the demand scenarios, which owns a certain effective interval.

A bi‐level approach to load restoration strategy considering variant length of time steps

A bi-level load restoration optimisation strategy is proposed for the transmission system with wind farm-energy storage combined systems (WESs), taking the variant length of time steps into account. The upper level model is proposed to maximise weighted restored loads, and formulated as a mixed-integer linear programming model. After solving the upper model, the optimal load pickup and transmission line restoration scheme can be obtained and delivered to the lower level model. The lower level model adopts a non-linear model to minimise the length of the current time step, which is delivered to the upper lever model. By iteratively solving the upper and lower level models, the optimal load pickup and transmission line restoration scheme as well as the optimal length of current time step can be obtained. To minimise the gap between the scheduled generation of the WES and its actual power generation, a real-time energy storage (ES) dispatch strategy is proposed taking maximum charging-discharging cycles of the ES into account. The entire load restoration strategy can be obtained by iteratively solving the proposed model with updated operational conditions of power systems. Finally, two test systems are employed to verify the validity and correctness of the proposed model.

Energy storage configuration and day-ahead pricing strategy for electricity retailers considering demand response profit

Real-time price(RTP) is one of the common methods for electricity retailers to adjust load demands and gain revenue from demand response(DR) programs. However, effectiveness and accuracy of the RTP directly affect the DR quantity, which reflects as revenue returns in DR programs. A method is proposed to maximize profit of the electricity retailer by configurating energy storage system(ESS) and coordinate the operation of ESS with RTP to participate in DR programs, which enriches the profitability measures of retailers. Firstly, an improved CNN-LSTM algorithm is utilized to establish the DR dynamic characteristic model with expected consumption as input and RTP as output. Thus, the demand curve during each period can be generated, from which optimal RTP with maximum profit of the retailer can be estimated. Secondly, a bi-level optimization model is established to maximize the profit from the DR program in a whole day. The upper level real-time pricing model takes maximum profit of the retailer as the objective by optimizing the RTP with fixed ESS configuration. The lower level ESS configuration model takes maximum profit of ESS as the objective with RTP fixed by the upper level model, from which the rated power, capacity and daily operation strategy of ESS can be optimized and sent to the upper level model. Finally, the optimal profit gained by the retailer can obtained by iterative calculation between the upper and lower level models. Based on the historical data from PJM market, the case demonstrates that the algorithm proposed can describe the DR dynamic characteristic effectively and accurately. Moreover, by configurating ESS, the retailer’s profit extra increases by 7.19%.